Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3613-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-3613-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A physics-based Antarctic melt detection technique: combining Advanced Microwave Scanning Radiometer 2, radiative-transfer modeling, and firn modeling
Marissa E. Dattler
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD 20740, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Brooke Medley
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
C. Max Stevens
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
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Ice sheets are covered by a firn layer, which is the transition stage between fresh snow and ice. Accurate modelling of firn density properties is important in many glaciological aspects. Current models show disagreements, are mostly calibrated to match specific observations of firn density and lack thorough uncertainty analysis. We use a novel calibration method for firn models based on a Bayesian statistical framework, which results in improved model accuracy and in uncertainty evaluation.
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Short summary
We developed an algorithm based on combining models and satellite observations to identify the presence of surface melt on the Antarctic Ice Sheet. We find that this method works similarly to previous methods by assessing 13 sites and the Larsen C ice shelf. Unlike previous methods, this algorithm is based on physical parameters, and updates to this method could allow the meltwater present on the Antarctic Ice Sheet to be quantified instead of simply detected.
We developed an algorithm based on combining models and satellite observations to identify the...